Last updated: 2019-12-02

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Knit directory: Comparative_APA/analysis/

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Unstaged changes:
    Modified:   analysis/CorrbetweenInd.Rmd
    Modified:   analysis/PASnumperSpecies.Rmd
    Modified:   analysis/annotationInfo.Rmd
    Modified:   analysis/multiMap.Rmd
    Modified:   analysis/verifyBAM.Rmd

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These are the previous versions of the R Markdown and HTML files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view them.

File Version Author Date Message
Rmd b5df813 brimittleman 2019-12-02 initial results no overlap
html 12a7fcb brimittleman 2019-11-20 Build site.
Rmd 6d6e7d5 brimittleman 2019-11-20 fix label
html 9b73cff brimittleman 2019-11-18 Build site.
Rmd 3c2512b brimittleman 2019-11-18 wflow_publish(“analysis/diffSplicing.Rmd”)
html 7770d55 brimittleman 2019-11-18 Build site.
Rmd fa2c075 brimittleman 2019-11-18 code for diff splice
html 106f3c1 brimittleman 2019-11-15 Build site.
Rmd 4ad7bce brimittleman 2019-11-15 look at results of cluster lift
html dc91b0a brimittleman 2019-11-11 Build site.
Rmd b5ba82e brimittleman 2019-11-11 add diff expression and diff splicing 
library(tidyverse)
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library(reshape2)

Attaching package: 'reshape2'
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    smiths

I want to use the RNA seq I collected to also perform a differential splicing analysis with leafcutter. I will follow the pipeline found at http://davidaknowles.github.io/leafcutter/articles/Usage.html. For a first pass I will use the bam files from the snakemake and differential expression analysis pipeline.

I will get clusters in both species then perform reciprocal liftover. I can use a liftover pipeline similar to the one I used for the differnetial PAS analysis.

Pipeline from example on leafcutter github.



for bamfile in `ls run/geuvadis/*chr1.bam`; do
    echo Converting $bamfile to $bamfile.junc
    samtools index $bamfile
    regtools junctions extract -a 8 -m 50 -M 500000 $bamfile -o $bamfile.junc
    echo $bamfile.junc >> test_juncfiles.txt
done

python ../clustering/leafcutter_cluster_regtools.py -j test_juncfiles.txt -m 50 -o testYRIvsEU -l 500000

At this point I will be able to liftover the junctions. I can use the human corrdinates for the differential splicing step.

../scripts/leafcutter_ds.R --num_threads 4 ../example_data/testYRIvsEU_perind_numers.counts.gz example_geuvadis/groups_file.txt

I now have my RNA seq for each species. I can write a script that runs the junctions for each species.

sbatch converBam2Junc.sh

Create a script that only keeps the number chromosomes (2A and 2B for chimp). This means I will not have any of the chimp contigs.

I should lift first then filter


mkdir ../data/DiffSplice_liftedJunc
sbatch liftJunctionFiles.sh

liftover changes the junction files..

For the humans I can simply filter the original junctions that pass the reciprocal liftover. For the chimps I need to figure out why the junctions change with liftover.

Junction file format from regtools: bed12 format: - chrom - start - end - junction name - score- number of reads supporing junction - strand - thick start (same as chrom start) - thick end (same as chrom end) - itemRgb - default 255,0,0 - block cound- number blocks, defauld 2 - block size - comma- separated list of block sizes, number of items in list corresponds to blockCount - block start- comma separated list of block starts, all block star positions should be calculated relative to chromStrat and the number of items in the list shoudl correspond to blockCount

I need to write a script that fixes the lifted files. I need to make column 9 255,0,0 and remove the commas from the last 2 columns.

I can impliment the fix in the filter file.

sbatch runFilterNumChroms.sh

Make clusters:

juncfiles=read.table("../data/DiffSplice_liftedJunc/BothSpec_juncfiles.txt", header = F)
humanFiles=juncfiles %>% slice(1:6)
write.table(humanFiles, "../data/DiffSplice_liftedJunc/Human_juncfiles.txt", quote = F, col.names = F, row.names = F)
chimpFiles=juncfiles %>% slice(7:12)
write.table(chimpFiles, "../data/DiffSplice_liftedJunc/Chimp_juncfiles.txt", quote = F, col.names = F, row.names = F)
sbatch quantJunc.sh

Now I can merge all of the culsters with: /project2/yangili1/yangili/leafcutter_scripts/merge_leafcutter_clusters.py

sbatch MergeClusters.sh
sbatch QuantMergedClusters.sh

Make the sample list:

combinedCounts=read.table("../data/DiffSplice_liftedJunc/MergeCombined_perind_numers.counts", header=T)
x=colnames(combinedCounts)
#YG-BM-S8-18499H-Total_S8_R1_001-sort.bam
indiv=as.data.frame(x)  %>%  separate(x, into=c("yg", "bm","lane", "sample", "total",  "sort", "bam"), sep="[.]") %>% mutate(sample=paste(yg, "-", bm, "-", lane, "-", sample, "-", total, "-", sort, ".", bam, sep="")) %>% select(sample) %>%  mutate(Species=ifelse(grepl("H",sample), "Human", "Chimp"))


write.table(indiv, "../data/DiffSplice_liftedJunc/groups_file.txt", quote = F, col.names = F, row.names = F, sep = "\t")

fix to -Total instead of .Total

sbatch DiffSplice.sh
counts=read.table('../data/DiffSplice_liftedJunc/MergeCombined_perind_numers.counts', header=T, check.names = F)
meta=read.table("../data/DiffSplice_liftedJunc/groups_file.txt", header=F, stringsAsFactors = F)
colnames(meta)[1:2]=c("sample","group")
counts=counts[,meta$sample]
#rownames(counts)

Error: cluster needs to be clu_###_sign ‘’’chr1:46368431:46369161:clu_386:+’’’

I am going to write a python work around to change the cluster format. This will take the unziped version of the counts file. When I run it, I can unzip and zip the results in the bash script.

There are either not enough samples or min coverage problems. Let me compare these to the numbers.

results=read.table("../data/DiffSplice_liftedJunc/MergedRes_cluster_significance.txt",stringsAsFactors = F, header = T, sep="\t") %>% separate(cluster, into=c("chrom", "clus"),sep=":")

counts=read.table("../data/DiffSplice_liftedJunc/MergeCombined_perind.counts.fixed.gz", header = T)

Problem is the recluster and quantify has all human and all chimp. I dont have any clusters with counts for both species…

##Old pipeline

Now I need to do reciprocal liftover with the clusters.

  • /project2/gilad/briana/Comparative_APA/Human/data/RNAseq/DiffSplice/
  • /project2/gilad/briana/Comparative_APA/Chimp/data/RNAseq/DiffSplice/

Chain files are in /data/chainFiles/ * panTro5ToHg38.over.chain
* hg38ToPanTro5.over.chain

I first need to make bedfiles with these clusters.

The clusters all have _NA I dont this this is correct.


<!-- gunzip ../Human/data/RNAseq/DiffSplice/humanJunc_perind.counts.gz -->

<!-- gunzip ../Chimp/data/RNAseq/DiffSplice/chimpJunc_perind.counts.gz -->


<!-- python cluster2bed.py ../Human/data/RNAseq/DiffSplice/humanJunc_perind.counts ../Human/data/RNAseq/DiffSplice/humanJunc.bed -->

<!-- python cluster2bed.py ../Chimp/data/RNAseq/DiffSplice/chimpJunc_perind.counts ../Chimp/data/RNAseq/DiffSplice/chimpJunc.bed -->


<!-- #I need to name the clusters before I can do the lift. (this is like the naming in apa 1-n(clusters)) -->
<!-- python nameClusters.py ../Human/data/RNAseq/DiffSplice/humanJunc.bed ../Human/data/RNAseq/DiffSplice/humanJuncNamed.bed -->

<!-- python nameClusters.py ../Chimp/data/RNAseq/DiffSplice/chimpJunc.bed ../Chimp/data/RNAseq/DiffSplice/chimpJuncNamed.bed -->

<!-- sbatch clusterLiftprimary.sh -->

<!-- sbatch clusterLiftReverse.sh -->

Evaluate results:

(this code is from the lift for the PAS)

# unliftedH=read.table("../Human/data/RNAseq/DiffSplice/humanJunc_unlifted.bed",stringsAsFactors = F) %>% nrow()
# unliftedC=read.table("../Chimp/data/RNAseq/DiffSplice/chimpJunc_unlifted.bed",stringsAsFactors = F) %>% nrow()
# 
# liftedH=read.table("../Human/data/RNAseq/DiffSplice/humanJunc_inChimp.bed",stringsAsFactors = F) %>% nrow()
# liftedC=read.table("../Chimp/data/RNAseq/DiffSplice/chimpJunc_inHuman.bed",stringsAsFactors = F) %>% nrow()
# 
# primaryUnC=c("Chimp","Unlifted", unliftedC)
# primaryUnH=c("Human","Unlifted", unliftedH)
# 
# primaryLH=c("Human","Lifted", liftedH)
# primaryLC=c("Chimp","Lifted", liftedC)
# 
# header=c("species", "liftStat", "PAS")
# primaryDF= as.data.frame(rbind(primaryLH,primaryLC, primaryUnH,primaryUnC)) 
# colnames(primaryDF)=header
# 
# 
# primaryDF$PAS=as.numeric(as.character(primaryDF$PAS)) 
# 
# 
# 
# primaryDF= primaryDF %>% group_by(species) %>% mutate(nPAS=sum(PAS)) %>% ungroup() %>% mutate(proportion=PAS/nPAS)
# ggplot(primaryDF,aes(x=species, y=PAS, fill=liftStat)) + geom_bar(stat="identity",position = "dodge") + scale_fill_brewer(palette = "Dark2") + labs(title="Primary Liftover Results", y="Isoforms")
# 
# ggplot(primaryDF,aes(x=species, y=proportion, fill=liftStat)) + geom_bar(stat="identity",position = "dodge") + scale_fill_brewer(palette = "Dark2") + labs(title="Primary Liftover Results")

Look at the lifted:

# OriginalHuman=read.table("../Human/data/RNAseq/DiffSplice/humanJunc.bed",stringsAsFactors = F) 
# liftedHuman=read.table("../Human/data/RNAseq/DiffSplice/humanJunc_inChimp.bed",stringsAsFactors = F) 
# 
# OriginalChimp=read.table("../Chimp/data/RNAseq/DiffSplice/chimpJunc.bed",stringsAsFactors = F)
# liftedChimp=read.table("../Chimp/data/RNAseq/DiffSplice/chimpJunc_inHuman.bed",stringsAsFactors = F)

Reverse lift:

# re_unliftedH=read.table("../Human/data/RNAseq/DiffSplice/humanJunc_inChimp_B2Human_unlifted.bed",stringsAsFactors = F) %>% nrow()
# re_unliftedC=read.table("../Chimp/data/RNAseq/DiffSplice/chimpJunc_inHuman_B2Chimp_unlifted.bed",stringsAsFactors = F) %>% nrow()
# 
# re_liftedH=read.table("../Human/data/RNAseq/DiffSplice/humanJunc_inChimp_B2Human.bed",stringsAsFactors = F) %>% nrow()
# re_liftedC=read.table("../Chimp/data/RNAseq/DiffSplice/chimpJunc_inHuman_B2Chimp.bed",stringsAsFactors = F) %>% nrow()
# 
# re_UnC=c("Chimp","Unlifted", re_unliftedC)
# re_UnH=c("Human","Unlifted", re_unliftedH)
# 
# re_LH=c("Human","Lifted", re_liftedH)
# re_LC=c("Chimp","Lifted", re_liftedC)
# 
# header=c("species", "liftStat", "PAS")
# re_DF= as.data.frame(rbind(re_LH,re_LC, re_UnH,re_UnC)) 
# colnames(re_DF)=header
# 
# 
# re_DF$PAS=as.numeric(as.character(re_DF$PAS)) 
# 
# 
# 
# re_DF= re_DF %>% group_by(species) %>% mutate(nPAS=sum(PAS)) %>% ungroup() %>% mutate(proportion=PAS/nPAS)
# ggplot(re_DF,aes(x=species, y=PAS, fill=liftStat)) + geom_bar(stat="identity",position = "dodge") + scale_fill_brewer(palette = "Dark2") + labs(title="Reverse Liftover Results", y="Isoforms")
# ggplot(re_DF,aes(x=species, y=proportion, fill=liftStat)) + geom_bar(stat="identity",position = "dodge") + scale_fill_brewer(palette = "Dark2")+ labs(title="Reverse Liftover Results")

How many lifted both ways?

# #human
# re_liftedH/nrow(OriginalHuman)
# #chimp
# re_liftedC/nrow(OriginalChimp)

The next step will be to find the corresponding clusters. This is important because I will need to get the quantifications for the same introns and clusters. To do this I will need to write code that looks for the intron location from the primary lift in the reverse lift.

For now I will only look at those introns identified in both species. I need to do this because I need junctions we have quantifications for in both species.

I can make files with the human and chimp coordintats for the clusters that lift both ways. I will have to number each cluster

Human cluser:

# 
# humanRevlift=read.table("../Human/data/RNAseq/DiffSplice/humanJunc_inChimp_B2Human.bed",stringsAsFactors = F,col.names = c("Hchr","Hstart", "Hend", "cluster", "score", "strand")) %>% select(-strand)
# 
# #number clusters
# humanRevlift %>% select(cluster) %>% unique() %>% nrow()
# 
# humanRevlift$score=as.character(humanRevlift$score)
# humanRevlift= humanRevlift %>%  mutate(Name=paste("Human", score, sep="_")) %>% select(-score)
# 
# 
# humanInChimp=read.table("../Human/data/RNAseq/DiffSplice/humanJunc_inChimp.bed",stringsAsFactors = F,col.names = c("Cchr","Cstart", "Cend", "cluster", "score", "strand"))%>% select(-strand)
# humanInChimp$score=as.character(humanInChimp$score)
# humanInChimp= humanInChimp %>%  mutate(Name=paste("Human", score, sep="_")) %>% select(-score)
# 
# 
# humanliftedBoth=humanRevlift %>% inner_join(humanInChimp, by=c("cluster", "Name"))

Chimp clusters:

# chimpRevLift=read.table("../Chimp/data/RNAseq/DiffSplice/chimpJunc_inHuman_B2Chimp.bed",stringsAsFactors = F,col.names = c("Cchr","Cstart", "Cend", "cluster", "score", "strand")) %>% select(-strand)
# chimpRevLift$score=as.character(chimpRevLift$score)
# chimpRevLift= chimpRevLift %>%  mutate(Name=paste("Chimp", score, sep="_")) %>% select(-score)
# chimpRevLift %>% select(cluster) %>% unique() %>% nrow()
# 
# 
# chimpInHuman=read.table("../Chimp/data/RNAseq/DiffSplice/chimpJunc_inHuman.bed",stringsAsFactors = F,col.names = c("Hchr","Hstart", "Hend", "cluster", "score", "strand"))%>% select(-strand) 
# chimpInHuman$score=as.character(chimpInHuman$score)
# chimpInHuman= chimpInHuman %>%  mutate(Name=paste("Chimp", score, sep="_")) %>% select(-score)
# 
# chimpliftedBoth=chimpRevLift %>% inner_join(chimpInHuman, by=c("cluster", "Name"))

Try to join these by the human and chimp coordinates

# AllClusters=chimpliftedBoth %>% inner_join(humanliftedBoth, by=c("Cchr", "Cstart","Cend", "Hchr", "Hstart", "Hend")) %>% mutate(ChimpName=paste(Cchr,Cstart,Cend, cluster.x, sep=":" ),HumanName=paste(Hchr,Hstart,Hend, cluster.y, sep=":" ) )
# 
# nrow(AllClusters)
# 
# AllClusters %>% select(cluster.x) %>% unique() %>% nrow()
# AllClusters %>% select(cluster.y) %>% unique() %>% nrow()

This means there are ~7k isoforms from about 3k genes. This is from the ~5k clusters I had before. I can move on with these using the human names.

I will have to go back and figure out how to call clusters for more genes.

I need to reformat these back into the counts format.

# #chr1:17055:17233:clu_1
# 
# AllClustersNames=AllClusters %>% select(HumanName, ChimpName)
# 
# ChimpCluster=read.table("../Chimp/data/RNAseq/DiffSplice/chimpJunc_perind_numers.counts.gz") %>% rownames_to_column(var="ChimpName")
# FilteredChimpCluster= ChimpCluster %>% inner_join(AllClustersNames, by="ChimpName")
# 
# #map human onto these
# 
# HumanCluster=read.table("../Human/data/RNAseq/DiffSplice/humanJunc_perind_numers.counts.gz") %>% rownames_to_column(var="HumanName")
# FilteredClusterBoth=HumanCluster %>% inner_join(FilteredChimpCluster, by="HumanName") %>% select(-ChimpName) 
# 
# FilteredClusterBothfixed=FilteredClusterBoth[!duplicated(FilteredClusterBoth$HumanName),]
# 
# 
# 
# 
# 
# 
# #create group file- this should have the name of the bams and the group
# Bams=as.data.frame(colnames(FilteredClusterBothfixed)) %>% mutate(Species=ifelse(grepl("H",colnames(FilteredClusterBothfixed)), "Human", "Chimp")) %>% slice(2:n())
# 
# #mkdir ../data/DiffSplice 
# write.table(Bams, "../data/DiffSplice/groups_file.txt", col.names = F, row.names = F, quote = F, sep="\t" )
# 
# 
# write.table(FilteredClusterBothfixed, "../data/DiffSplice/BothSpec_perind.counts", col.names = T, row.names = F, quote = F, sep="\t" )
#

Remove the first name in header and zip the file:

(manually)

<!-- vi ../data/DiffSplice/BothSpec_perind.counts -->

<!-- gzip ../data/DiffSplice/BothSpec_perind.counts -->

I will run the differential splicing analysis with the human exon file for now. Download the NCBI refseq exons from the table browser. I also downloaded the names with the common names so I can fix the exon file.

<!-- python processhg38exons.py -->
<!-- gzip ../data/DiffSplice/hg38_ncbiRefseq_exonsfixed -->

Run leafcutter with python 2

<!-- sbatch DiffSplice.sh -->

Look at results:

# sig=read.table("../data/DiffSplice/leafcutter_ds_cluster_significance.txt",sep="\t" ,header =T,stringsAsFactors = F) %>% filter(status=="Success") 
# 
# sig$p.adjust=as.numeric(as.character(sig$p.adjust))
# 
# 
# qqplot(-log10(runif(nrow(sig))), -log10(sig$p.adjust),ylab="-log10 Total Adjusted Leafcutter pvalue", xlab="-log 10 Uniform expectation", main="Leafcutter Differential Splicing")
# abline(0,1)
# 
# sig %>% filter(p.adjust < .05 ) %>% nrow()

Use the leafcutter tool to visualize

<!-- sbatch DiffSplicePlots.sh -->

try with gencode exons:

<!-- sbatch DiffSplice_gencode.sh -->
<!-- sbatch DiffSplicePlots_gencode.sh -->
# sig_gencode=read.table("../data/DiffSplice/Gencode__cluster_significance.txt",sep="\t" ,header =T,stringsAsFactors = F) %>% filter(status=="Success") 
# 
# sig_gencode$p.adjust=as.numeric(as.character(sig_gencode$p.adjust))
# 
# 
# qqplot(-log10(runif(nrow(sig_gencode))), -log10(sig_gencode$p.adjust),ylab="-log10 Total Adjusted Leafcutter pvalue", xlab="-log 10 Uniform expectation", main="Leafcutter Differential Splicing Gencode exons")
# abline(0,1)
# 
# sig_gencode %>% filter(p.adjust < .05 ) %>% nrow()

sessionInfo()
R version 3.5.1 (2018-07-02)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)

Matrix products: default
BLAS/LAPACK: /software/openblas-0.2.19-el7-x86_64/lib/libopenblas_haswellp-r0.2.19.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] reshape2_1.4.3  forcats_0.3.0   stringr_1.3.1   dplyr_0.8.0.1  
 [5] purrr_0.3.2     readr_1.3.1     tidyr_0.8.3     tibble_2.1.1   
 [9] ggplot2_3.1.1   tidyverse_1.2.1

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.2       cellranger_1.1.0 plyr_1.8.4       compiler_3.5.1  
 [5] pillar_1.3.1     later_0.7.5      git2r_0.26.1     workflowr_1.5.0 
 [9] tools_3.5.1      digest_0.6.18    lubridate_1.7.4  jsonlite_1.6    
[13] evaluate_0.12    nlme_3.1-137     gtable_0.2.0     lattice_0.20-38 
[17] pkgconfig_2.0.2  rlang_0.4.0      cli_1.1.0        rstudioapi_0.10 
[21] yaml_2.2.0       haven_1.1.2      withr_2.1.2      xml2_1.2.0      
[25] httr_1.3.1       knitr_1.20       hms_0.4.2        generics_0.0.2  
[29] fs_1.3.1         rprojroot_1.3-2  grid_3.5.1       tidyselect_0.2.5
[33] glue_1.3.0       R6_2.3.0         readxl_1.1.0     rmarkdown_1.10  
[37] modelr_0.1.2     magrittr_1.5     whisker_0.3-2    backports_1.1.2 
[41] scales_1.0.0     promises_1.0.1   htmltools_0.3.6  rvest_0.3.2     
[45] assertthat_0.2.0 colorspace_1.3-2 httpuv_1.4.5     stringi_1.2.4   
[49] lazyeval_0.2.1   munsell_0.5.0    broom_0.5.1      crayon_1.3.4